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README.md

@@ -5,7 +5,7 @@ tags:
 - 奇虎360
 - RAG-reranking
 model-index:
-- name: 360Zhinao-1_8B-reranking
+- name: 360Zhinao-1.8B-reranking
   results:
   - task:
       type: Reranking
@@ -66,11 +66,11 @@ library_name: transformers
 <br>
 
 # MTEB Leaderboard Chinese Reranking Results
-We have validated the performance of our model on the [mteb-chinese-reranking leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Currently, the open-source models on this leaderboard are primarily bidirectional discriminative models (BERT-like models). The only unidirectional generative model (GPT-like model) is gte-Qwen1.5-7B-instruct, which has an average score of 66.38, ranking 25th, with less than ideal results. Our self-developed unidirectional generative model, zhinao_1-8b_reranking, achieved an average score of 70.13, currently ranking first overall and first among open-source models, opening up new possibilities for generative models to undertake discriminative tasks.
+We have validated the performance of our model on the [mteb-chinese-reranking leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Currently, the open-source models on this leaderboard are primarily bidirectional discriminative models (BERT-like models). The only unidirectional generative model (GPT-like model) is gte-Qwen1.5-7B-instruct, which has an average score of 66.38, ranking 25th, with less than ideal results. Our self-developed unidirectional generative model, 360Zhinao-1.8B-reranking, achieved an average score of 70.13, currently ranking first overall and first among open-source models, opening up new possibilities for generative models to undertake discriminative tasks.
 
 | Model | T2Reranking | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |  
 |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|  
-| **360Zhinao-1_8B-Reranking** | **68.55** | **37.29** | **86.75** | **87.92** | **70.13** |
+| **360Zhinao-1.8B-Reranking** | **68.55** | **37.29** | **86.75** | **87.92** | **70.13** |
 | piccolo-large-zh-v2 | 67.15 | 33.39 | 90.14 | 89.31 | 70 |
 | Baichuan-text-embedding | 67.85 | 34.3 | 88.46 | 88.06 | 69.67 |
 | stella-mrl-large-zh-v3.5-1792d | 66.43 | 28.85 | 89.18 | 89.33 | 68.45 |
@@ -102,7 +102,7 @@ FLASH_ATTENTION_FORCE_BUILD=TRUE ./miniconda3/bin/python -m pip install flash-at
 
 # Model Introduction
 
-The zhinao_1-8b_reranking model utilizes the self-developed zhinao_1-8b_base model as its foundation. Through iterative discovery and resolution of the following technical issues, it continuously stimulates the world knowledge inherent in the large model during the pre-training phase, better bridging the gap between generative models and discriminative tasks.
+The 360Zhinao-1.8B-reranking model utilizes the self-developed zhinao_1-8b_base model as its foundation. Through iterative discovery and resolution of the following technical issues, it continuously stimulates the world knowledge inherent in the large model during the pre-training phase, better bridging the gap between generative models and discriminative tasks.
 
 ## Data Processing
 
@@ -278,7 +278,7 @@ class FlagRerankerCustom:
 
 
 if __name__ == "__main__":
-    model_name_or_path = "360Zhinao-1_8B-Reranking"
+    model_name_or_path = "360Zhinao-1.8B-Reranking"
     model = FlagRerankerCustom(model_name_or_path, use_fp16=False)
     inputs=[["What Color Is the Sky","Blue"], ["What Color Is the Sky","Pink"],]
     ret = model.compute_score(inputs)